The authors applied semi‑supervised deep‑learning to super‑resolution images of modern and fossil grass pollen, training convolutional neural networks to extract abstract morphological features. These features were used to quantify past grass community diversity and C3:C4 ratios in a 25,000‑year lake‑sediment record, revealing a marked diversity loss during the last glacial and a gradual decline of C4 grasses in the Holocene.

The authors introduce AdaPoinTr, a geometry-aware transformer that predicts the alpha‑shape of coniferous tree crowns from incomplete terrestrial or mobile laser‑scanning point clouds, focusing on crown reconstruction rather than full tree completion. Trained on synthetically generated partial crowns, the model consistently improves crown shape similarity and reduces height estimation bias across three diverse forest datasets, providing a cost‑effective solution for enhanced 3D forest structural monitoring.

The review examines the genetic networks governing spikelet number per spike (SNS) in wheat, highlighting how the balance between inflorescence meristem activity and the timing of terminal spikelet transition determines yield potential. It discusses how mutations affecting meristem identity can create supernumerary spikelets, the trade-offs of such traits, and recent advances using spatial transcriptomics, single‑cell analyses, and multi‑omics to identify new SNS genes for breeding.

The study reveals that rice perceives Xanthomonas oryzae pv. oryzae outer membrane vesicles through a rapid calcium signal that triggers plasma‑membrane nanodomain formation and the re‑organisation of defence‑related proteins, establishing an early immune response. Without this Ca2+ signal, OMVs are not recognized and immunity is weakened.

The study compares the iron-poor oceanic diatom Thalassiosira oceanica with the iron-rich coastal species T. pseudonana to uncover how diatoms adapt to low-iron conditions. Using photo‑physiological measurements, proteomic profiling, and focused ion beam scanning electron microscopy, the researchers show that each species remodels chloroplast compartments and exhibits distinct mitochondrial architectures to maintain chloroplast‑mitochondrial coupling under iron limitation.

The study demonstrates that the plastid chaperone CLPC2, but not its paralogue CLPC1, is essential for Arabidopsis responsiveness to microbial volatile compounds and for normal seed and seedling development. Loss of CLPC2 alters the chloroplast proteome, affecting proteins linked to growth, photosynthesis, and embryogenesis, while overexpression of CLPC2 mimics CLPC1 deficiency, highlighting distinct functional roles within the CLP protease complex.

The study investigated how barley (Hordeum vulgare) adjusts mitochondrial respiration under salinity stress using physiological, biochemical, metabolomic and proteomic approaches. Salt treatment increased respiration and activated the canonical TCA cycle, while the GABA shunt remained largely inactive, contrasting with wheat responses.

The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

The study applied dual-species spatial transcriptomics at single-cell resolution to map plant and fungal gene activity in rice roots colonized by Rhizophagus irregularis, revealing transcriptional heterogeneity among morphologically similar arbuscules. By pioneering an AM-inducible TRAP-seq using stage‑specific promoters, the authors uncovered stage‑specific reprogramming of nutrient transporters and defence genes, indicating dynamic regulation of nutrient exchange and arbuscule lifecycle.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.